Analyzing flow of objects or data at different granularities of space and time can unveil interesting insights or trends. For example, transportation companies, by aggregating passenger travel data (e.g., counting passengers traveling from one region to another), can analyze movement behavior. In this paper, we study the problem of finding important trends in passenger movements between regions at different granularities. We define Origin (O), Destination (D), and Time (T ) patterns (ODT patterns) and propose a bottom-up algorithm that enumerates them. We suggest and employ optimizations that greatly reduce the search space and the computational cost of pattern enumeration. We also propose pattern variants (constrained patterns and top-k patterns) that could be useful to different applications scenarios. Finally, we propose an approximate solution that fast identifies ODT patterns of specific sizes, following a generate-and-test approach. We evaluate the efficiency and effectiveness of our methods on three real datasets and showcase interesting ODT flow patterns in them.

翻译：在不同空间和时间粒度上分析物体或数据的流动能够揭示有价值的洞察或趋势。例如，交通公司通过聚合乘客出行数据（如统计从一个区域前往另一区域的乘客数量），可以分析移动行为。本文研究在不同粒度下发现区域间乘客流动重要趋势的问题。我们定义了起点（O）、终点（D）和时间（T）模式（ODT模式），并提出一种自底向上算法对其进行枚举。我们提出并采用了多种优化方法，显著减少了模式枚举的搜索空间和计算成本。同时，我们提出了可能适用于不同应用场景的模式变体（约束模式和top-k模式）。最后，基于生成-测试框架，我们提出了一种近似解决方案，能够快速识别特定规模的ODT模式。我们在三个真实数据集上评估了所提方法的效率与有效性，并展示了其中具有启发性的ODT流模式。